摘要
提出了分形信号的小波分解与重构的一种快速算法。针对分形信号的自相似和长时相关的特点,采用离散小波变换(DWT)对分形信号进行多尺度分解,使其成为各尺度上的近似平稳信号,从而可利用通常的Wiener滤波或Kalman滤波方法进行估计,然后再由DWT进行多尺度重构,估计出被噪声污染了的原始信号。重点对DWT的滤波过程进行算法设计,并估计了计算复杂度。
In this paper, a fast algorithm for the fractal signal wavelet decomposition and reconstruction is put foreward. In accordance with the self similar and long-term related characteristics ofthe fractal signals, and by means of discrete wavelet transformation (DWT), multi-scale decompositionis carried out so as to make them become similar stationary signals and estimate them with the usualwiener filtering and Kalman filtering method,Then multi-scale reconstruction is carried out with DWTin order to estimate the primary signals polluted by noises. This paper stresses the algorithm design ofthe DWT filtering process, and the computing complexity is also considered.
出处
《国防科技大学学报》
EI
CAS
CSCD
1998年第1期103-108,共6页
Journal of National University of Defense Technology
基金
九五国防预研基金
国防科技大学试验技术项目
关键词
分形信号
离散小波变换
滤波算法
信号处理
fractal signal, discrete wavelet transform, Fast Fourier transform, filter